An Efficient Virtual Machine Placement via Bin Packing in Cloud Data Centers

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 926)


Virtual machine (VM) consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM consolidation includes a most important subproblem, i.e., VM placement problem. The basic objective of VM placement is to minimize the use of running physical machines (PMs). An enhanced levy based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving VM placement problem. Moreover, the best fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are performed to check the performance of the proposed algorithm. The proposed algorithm is compared with simple particle swarm optimization (PSO) and the hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. Matlab is used for simulations.


Cloud computing Particle swarm optimization Levy flight algorithm Virtual machine placement Variable sized bin packing 


  1. 1.
    Kong, Y., Zhang, M., Ye, D.: A belief propagation-based method for task allocation in open and dynamic cloud environments. Knowl.-Based Syst. 115, 123–132 (2017)CrossRefGoogle Scholar
  2. 2.
    Guo, Y., Stolyar, A., Walid, A.: Online VM auto-scaling algorithms for application hosting in a cloud. IEEE Trans. Cloud Comput. (2018, accepted)Google Scholar
  3. 3.
    Fu, X., Chen, J., Deng, S., Wang, J., Zhang, L.: Layered virtual machine migration algorithm for network resource balancing in cloud computing. Front. Comput. Sci. 12(1), 75–85 (2018)CrossRefGoogle Scholar
  4. 4.
    Abdel-Basset, M., Abdle-Fatah, L., Sangaiah, A.K.: An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Cluster Comput. 1–16 (2018)Google Scholar
  5. 5.
    Jensi, R., Jiji, G.W.: An enhanced particle swarm optimization with levy flight for global optimization. Appl. Soft Comput. 43, 248–261 (2016)CrossRefGoogle Scholar
  6. 6.
    Mirjalili, S., Saremi, S., Mirjalili, S.M., dos S. Coelho, L.: Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst. Appl. 47, 106–119 (2016)CrossRefGoogle Scholar
  7. 7.
    Khosravi, A., Andrew, L.L.H., Buyya, R.: Dynamic VM placement method for minimizing energy and carbon cost in geographically distributed cloud data centers. IEEE Trans. Sustain. Comput. 2(2), 183–196 (2017)CrossRefGoogle Scholar
  8. 8.
    Chekired, D.A., Khoukhi, L.: Smart grid solution for charging and discharging services based on cloud computing scheduling. IEEE Trans. Ind. Inform. 13(6), 3312–3321 (2017)CrossRefGoogle Scholar
  9. 9.
    Cao, Z., Lin, J., Wan, C., Song, Y., Zhang, Y., Wang, X.: Optimal cloud computing resource allocation for demand side management in smart grid. IEEE Trans. Smart Grid 8(4), 1943–1955 (2017)Google Scholar
  10. 10.
    Wang, H., Tianfield, H.: Energy-aware dynamic virtual machine consolidation for cloud datacenters. IEEE Access 6, 15259–15273 (2018)CrossRefGoogle Scholar
  11. 11.
    Javaid, N., Javaid, S., Abdul, W., Ahmed, I., Almogren, A., Alamri, A., Niaz, I.A.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)CrossRefGoogle Scholar
  12. 12.
    Zhou, A., Wang, S., Cheng, B., Zheng, Z., Yang, F., Chang, R.N., Lyu, M.R., Buyya, R.: Cloud service reliability enhancement via virtual machine placement optimization. IEEE Trans. Serv. Comput. 10(6), 902–913 (2017)CrossRefGoogle Scholar
  13. 13.
    Moreno-Vozmediano, R., Montero, R.S., Huedo, E., Llorente, I.M.: Orchestrating the deployment of high availability services on multi-zone and multi-cloud scenarios. J. Grid Comput. 16(1), 39–53 (2018)CrossRefGoogle Scholar
  14. 14.
    Vakilinia, S.: Energy efficient temporal load aware resource allocation in cloud computing datacenters. J. Cloud Comput. 7(1), 2 (2018)CrossRefGoogle Scholar
  15. 15.
    Zahoor, S., Javaid, S., Javaid, N., Ashraf, M., Ishmanov, F., Afzal, M.: Cloud fog based smart grid model for efficient resource management. Sustainability 10(6), 2079 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.COMSATS University IslamabadIslamabadPakistan
  2. 2.Farasan Networking Research Laboratory, Department of Computer Science and Information SystemJazan UniversityJazanSaudi Arabia
  3. 3.Virtual University of Pakistan, Kotli CampusAzad KashmirPakistan
  4. 4.Mohi-ud-Din Islamic University Nerian SharifAzad Jammu and KashmirPakistan

Personalised recommendations